{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from  scipy import stats\n",
    "from matplotlib import pyplot as plt\n",
    "import  seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1., 18., 19., 25., 40., 46., 67., 46., 69., 74., 69., 61., 58.,\n",
       "        45., 64., 51., 39., 37., 30., 29., 21., 19., 13., 10., 12.,  5.,\n",
       "         6.,  5.,  6.,  5.,  2.,  2.,  2.,  0.,  0.,  1.,  1.,  0.,  1.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
       " array([ 1.55797732,  2.2336176 ,  2.90925787,  3.58489815,  4.26053842,\n",
       "         4.93617869,  5.61181897,  6.28745924,  6.96309952,  7.63873979,\n",
       "         8.31438007,  8.99002034,  9.66566061, 10.34130089, 11.01694116,\n",
       "        11.69258144, 12.36822171, 13.04386199, 13.71950226, 14.39514253,\n",
       "        15.07078281, 15.74642308, 16.42206336, 17.09770363, 17.77334391,\n",
       "        18.44898418, 19.12462445, 19.80026473, 20.475905  , 21.15154528,\n",
       "        21.82718555, 22.50282582, 23.1784661 , 23.85410637, 24.52974665,\n",
       "        25.20538692, 25.8810272 , 26.55666747, 27.23230774, 27.90794802,\n",
       "        28.58358829, 29.25922857, 29.93486884, 30.61050912, 31.28614939,\n",
       "        31.96178966, 32.63742994, 33.31307021, 33.98871049, 34.66435076,\n",
       "        35.33999103]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = stats.chi2(10).rvs(1000)\n",
    "plt.hist(a, bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3989422804014327"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dist.pdf  概率密度函数\n",
    "stats.norm(0, 1).pdf(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dis.cdf 累计分布函数"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "衍生特征   \n",
    "1. 偏度\n",
    "SKEWBESS\n",
    "\n",
    "2. kurtosis  峰度\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2006d494550>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x = np.random.normal(size = 10000)\n",
    "sns.distplot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.moment(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2006d5d1f10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = stats.chi2(10).rvs(10000)\n",
    "sns.distplot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
